This specification relates to processing local entity content.
The Internet provides access to a wide variety of resources such as video or audio files, web pages for particular subjects, book articles, or news articles. A search system can identify resources in response to a search query that includes one or more search phrases (i.e., one or more words). The search system ranks the resources based on their relevance to the search query and on measures of quality of the resources and provides search results that link to the identified resources. The search results are typically ordered for viewing according to the rank.
Some search systems can obtain or infer a location of a user device from which a search query was received and include local search results that are responsive to the search query. A local search result is a search result that references a document that describes a local entity. A local entity, in turn, is an entity that has been classified as having local significance to particular location. Local entities are typically physical entities associated with an address or a region, such as a restaurant, a hospital, a landmark, and the like. A search result referencing a document describing a local entity receives a search score “boost” for a query if the location associated with the local entity is near the location of the user device. For example, in response to a search query for “coffee shop.” the search system may provide local search results that reference web pages for coffee shops near the location of the user device. Many users in various geographic regions will likely be satisfied with receiving local results for coffee shops in response to the search query “coffee shop” because it is likely that a user submitting the query “coffee shop” is interested in search results for coffee shops that are local to the user's location.
Some search systems also provide search results based on similarity of the subject matter that the documents describe. In the context of local entities, for example, some search systems may use several or more different algorithms to generate lists of similar local entities. The algorithms need not necessarily take into account location. For example, entities, whether local or not, may be categorized as being similar based on one or more of the following signals: keyword descriptors; query term click fractions; search result selection co-occurrence; site analysis; and so on. Because each algorithm uses a different set of signals and implements different processing steps, each algorithm, for a particular local entity, may produce a different set of entities that are determined to be related or similar to the particular local entity. For example, a particular algorithm may be tuned to generate, for a particular restaurant entity, a list of other entities that provide the same or very similar menu items. Likewise, another algorithm may be tuned to generate, for the particular entity, a list of other entities for which users of a particular demographic exhibit similar click behavior for a set of query terms.